Role classification for networks based on transfer learning

Transfer learning refers to a class of machine learning methods concerned with transferring knowledge learned from one or more domains (the sources) to be applied to a given domain (the target). In network data mining, transfer learning makes sense, because each network is naturally a domain. We describe different settings of transfer learning. We address the problem of node classification according to their roles. We demonstrate an existing algorithm, RolX, which uses transductive transfer learning to learn a classifier from a network (in which roles of nodes are known), and identify roles of nodes in another network. We are seeking optimization and/or new algorithms, due to several shortcomings of RolX.